Pangram Labs’ mission is to protect authenticity by building the world’s best AI detection systems. We publish research on state-of-the-art AI detection techniques and algorithms, and build products to bring our technology into people's daily lives.
We are looking for high agency, mission-driven, and passionate builders to join our team. All roles are in person in Brooklyn, New York.
About the Role
Pangram Labs is hiring Technical Content Specialist to join our team. You will be responsible for producing and maintaining Pangram's technical and user-facing content across every channel — from peer-facing research papers to model cards, benchmark evaluations, and technical reports.
This is not a general writing role: the position requires independently comprehending our model's architecture, training methodology, and evaluation results, and communicating statistical concepts (such as false positive rates and the effect of prevalence on accuracy) correctly to both technical and general audiences.
You will work closely with our research and engineering teams and serves as a guardian of technical accuracy across all published material.
KEY RESPONSIBILITIES
- Write and revise technical papers on the Pangram AI model, including content covering model architecture, training methodology, and evaluation results.
- Write technical reports on Pangram's studies on the prevalence of AI-generated content on the internet and in various domains. These reports will cover methodology, results, and analysis.
- Create user-facing content such as guides and FAQ content that guide product usage and the interpretation of detection results, including metrics such as false positive rates and the effect of prevalence on accuracy.
- Create clear explanations of how to interpret detection results — confidence scores, thresholds, false-positive/false-negative trade-offs — for non-technical audiences.
- Document our methodology, model behavior, and known limitations in a way that’s accurate to the research and accessible to users.
- Partner with researchers and engineers to turn benchmarks, evaluations, and model updates into published documentation.
MINIMUM QUALIFICATIONS
- Bachelor's degree (or higher) in Computer Science, Mathematics, Statistics, or a closely related quantitative field.
- Demonstrated ability to understand and accurately communicate machine-learning concepts, including model architecture, training methodology, and statistical evaluation.
- Strong command of applied statistics and probability, including false positive/negative rates, precision and recall, and base-rate/prevalence effects on predictive accuracy.
- Excellent written communication skills, with the ability to adapt technical content for both expert and general audiences.